Abstract

Channel impulse response (CIR)-assisted visible light positioning (VLP) improves indoor ranging by separating the line-of-sight (LoS) contribution from multipathcontaminated observations. Practical CIR processing, however, does not provide an ideal LoS measurement: delay-bin aggregation, path-support errors, tap-estimation noise, and residual non-line-of-sight leakage introduce a structured bias in the estimated LoS power ratio (LPR). This Letter formulates bias-limited VLP as a task-guided measurement-calibration problem. Instead of replacing the localization backend with coordinate regression, the proposed task-guided LoS power-ratio calibration (T-LPRCal) framework learns a bounded correction of the CIR-derived LPR before range estimation. A first-order propagation model relates LPR bias to range and position errors, while a compact recurrent classifier selects the correction through position-supervised labels generated offline by the physical localization pipeline. Simulations in a representative indoor optical channel show that T-LPRCal reduces the bias floor in the high signal-to-noise ratio (SNR) regime, improves median and 90th-percentile position errors, and preserves explicit Lambertian ranging and a least-squares (LS) localization backend.